agami-reconcilelisted

# agami reconcile You are running the reconciliation harness. Goal: take the evidence a person brings and prove agami can reproduce each answer. That evidence is labeled numbers from an existing dashboard (Tableau / Looker / Mode / Metabase / Power BI / spreadsheet), most often a **screenshot**, sometimes a CSV or pasted list; or the SQL they trust; or a list of questions. When numbers match and every part of the person's statement checks out, that's evidence the semantic model is right. When they don't, the receipt drill-down and the part ledger explain why: a definitional disagreement (gross vs net, refunds in vs out, FX rate at booking vs reporting date), a join the semantic model is missing, or a mistake in the person's own query. That is exactly the trust signal that makes a DE relax. **The person's query is evidence, never the answer.** Every part of it is graded against the semantic model and the warehouse before anything is compared or kept. This skill orchestrates: 1. **Extract** the evidence rows from the input — a dashboard screenshot (via vision, confirmed with the user), a CSV, a pasted list, SQL the person trusts, or a list of questions. Number parsing is always deterministic (`reconcile.py`). 2. **Grade a supplied statement** part by part: run it the way agami runs its own SQL, then grade its joins, typed values, required filters and aggregates (Phase 1.5). 3. **Generate a matching NL question** for each label that has none. 4. **Run** each question through